Comparative Performance of Three Machine Learning Models in Predicting Influent Flow Rates and Nutrient Loads at Wastewater Treatment Plants

Accurately predicting influent wastewater quality is vital for the efficient operation and maintenance of wastewater treatment plants (WWTPs). This study evaluated three machine learning (ML) models for predicting influent flow rates and nutrient loads of both industrial and domestic wastewaters in WWTPs. These predictions were based on meteorological data and the population migration patterns. The models─random forest, extra trees, and gradient boosting regressor─were successfully applied to three full-scale WWTPs in Shenzhen, China. All the models demonstrated robust performance in predicting influent flow rate, ammoniacal nitrogen (NH3–N), and total nitrogen (TN). Feature importance analysis revealed that the average precipitation over the past n days and population migration were the most influential factors for predicting influent flow rate. Conversely, human activities have a greater impact on pollutant concentrations. Scenario analyses indicated that precipitation contributed to approximately 5%–10% of the wastewater influent, while groundwater infiltration accounted for around 20%. Overall, this study provides a model framework for forecasting wastewater loads using meteorological and population migration data, setting the groundwork for smart management in WWTPs.